
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[int]       	=      (UnCLIPScheduler,)
	def 		_lowercase    (			self    :     Union[str, Any]			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	List[Any]:
					lowercase_	    =   {
					    '''num_train_timesteps''': 1_0_0_0,
					    '''variance_type''': '''fixed_small_log''',
					    '''clip_sample''': True,
					    '''clip_sample_range''': 1.0,
					    '''prediction_type''': '''epsilon''',
					}

					config.update(**SCREAMING_SNAKE_CASE_			)
					return config
	def 		_lowercase    (			self    :     List[Any]			)			->	Any:
					for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     int			)			->	Any:
					for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	List[str]:
					for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			)			->	List[Any]:
					for clip_sample_range in [1, 5, 1_0, 2_0]:
									self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     str			)			->	Any:
					for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			)			->	Dict:
					for time_step in [0, 5_0_0, 9_9_9]:
									for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
													if prev_timestep is not None and prev_timestep >= time_step:
																	continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_			,				prev_timestep=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[Any]			)			->	Any:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config(variance_type='''fixed_small_log'''			)
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0000e-10			)			) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.0_54_96_25			)			) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.9_99_49_87			)			) < 1e-5
	def 		_lowercase    (			self    :     Optional[int]			)			->	List[Any]:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config(variance_type='''learned_range'''			)
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   0.5

					assert scheduler._get_variance(1			,				predicted_variance=SCREAMING_SNAKE_CASE_			) - -10.1_71_27_90 < 1e-5
					assert scheduler._get_variance(4_8_7			,				predicted_variance=SCREAMING_SNAKE_CASE_			) - -5.7_99_80_52 < 1e-5
					assert scheduler._get_variance(9_9_9			,				predicted_variance=SCREAMING_SNAKE_CASE_			) - -0.0_01_00_11 < 1e-5
	def 		_lowercase    (			self    :     Optional[int]			)			->	Tuple:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config()
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   scheduler.timesteps

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter
					lowercase_	    =   torch.manual_seed(0			)

					for i, t in enumerate(SCREAMING_SNAKE_CASE_			):
									# 1. predict noise residual
									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									# 2. predict previous mean of sample x_t-1
									lowercase_	    =   scheduler.step(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				generator=SCREAMING_SNAKE_CASE_			).prev_sample

									lowercase_	    =   pred_prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					assert abs(result_sum.item() - 2_52.2_68_24_95			) < 1e-2
					assert abs(result_mean.item() - 0.3_28_47_43			) < 1e-3
	def 		_lowercase    (			self    :     Any			)			->	Optional[int]:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config()
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					scheduler.set_timesteps(2_5			)

					lowercase_	    =   scheduler.timesteps

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter
					lowercase_	    =   torch.manual_seed(0			)

					for i, t in enumerate(SCREAMING_SNAKE_CASE_			):
									# 1. predict noise residual
									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									if i + 1 == timesteps.shape[0]:
													lowercase_	    =   None
									else:
													lowercase_	    =   timesteps[i + 1]

									# 2. predict previous mean of sample x_t-1
									lowercase_	    =   scheduler.step(
									    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				prev_timestep=SCREAMING_SNAKE_CASE_			,				generator=SCREAMING_SNAKE_CASE_			).prev_sample

									lowercase_	    =   pred_prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					assert abs(result_sum.item() - 2_58.2_04_49_83			) < 1e-2
					assert abs(result_mean.item() - 0.3_36_20_38			) < 1e-3
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Tuple:
					pass




	def 		_lowercase    (			self    :     List[Any]			)			->	Tuple:
					pass






"""simple docstring"""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       torch.FloatTensor





class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase						):






       @register_to_config
       def __init__(	self			:				int							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				Tuple[str] = ("DownEncoderBlock2D",)							,   UpperCAmelCase_			:				Tuple[str] = ("UpDecoderBlock2D",)							,   UpperCAmelCase_			:				Tuple[int] = (64,)							,   UpperCAmelCase_			:				int = 1							,   UpperCAmelCase_			:				str = "silu"							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 32							,   UpperCAmelCase_			:				int = 256							,   UpperCAmelCase_			:				int = 32							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				float = 0.18215							,   UpperCAmelCase_			:				str = "group"							,   )->	Optional[Any]:







              """simple docstring"""
              super().__init__()

              # pass init params to Encoder
              UpperCamelCase       			=		Encoder(
                  in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   down_block_types=UpperCAmelCase_							,   block_out_channels=UpperCAmelCase_							,   layers_per_block=UpperCAmelCase_							,   act_fn=UpperCAmelCase_							,   norm_num_groups=UpperCAmelCase_							,   double_z=UpperCAmelCase_							,   )

              UpperCamelCase       			=		vq_embed_dim if vq_embed_dim is not None else latent_channels

              UpperCamelCase       			=		nn.Convad(UpperCAmelCase_							,   UpperCAmelCase_							,   1      )
              UpperCamelCase       			=		VectorQuantizer(UpperCAmelCase_							,   UpperCAmelCase_							,   beta=0.25							,   remap=UpperCAmelCase_							,   sane_index_shape=UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Convad(UpperCAmelCase_							,   UpperCAmelCase_							,   1      )

              # pass init params to Decoder
              UpperCamelCase       			=		Decoder(
                  in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   up_block_types=UpperCAmelCase_							,   block_out_channels=UpperCAmelCase_							,   layers_per_block=UpperCAmelCase_							,   act_fn=UpperCAmelCase_							,   norm_num_groups=UpperCAmelCase_							,   norm_type=UpperCAmelCase_							,   )






       @apply_forward_hook
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				bool = True      )->	VQEncoderOutput:







              """simple docstring"""
              UpperCamelCase       			=		self.encoder(UpperCAmelCase_      )
              UpperCamelCase       			=		self.quant_conv(UpperCAmelCase_      )

              if not return_dict:
                     return (h,)

              return VQEncoderOutput(latents=UpperCAmelCase_      )






       @apply_forward_hook
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = True      )->	Union[DecoderOutput, torch.FloatTensor]:







              """simple docstring"""
              # also go through quantization layer
              if not force_not_quantize:
                     UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.quantize(UpperCAmelCase_      )
              else:
                     UpperCamelCase       			=		h
              UpperCamelCase       			=		self.post_quant_conv(UpperCAmelCase_      )
              UpperCamelCase       			=		self.decoder(UpperCAmelCase_							,   quant if self.config.norm_type == "spatial" else None      )

              if not return_dict:
                     return (dec,)

              return DecoderOutput(sample=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				bool = True      )->	Union[DecoderOutput, torch.FloatTensor]:







              """simple docstring"""
              UpperCamelCase       			=		sample
              UpperCamelCase       			=		self.encode(UpperCAmelCase_      ).latents
              UpperCamelCase       			=		self.decode(UpperCAmelCase_      ).sample

              if not return_dict:
                     return (dec,)

              return DecoderOutput(sample=UpperCAmelCase_      )





